Abstract

This paper presents heart sound analysis method based on Time-Frequency Distribution (TFD) analysis and Mel Frequency Cepstrum Coefficient (MFCC). TFD represents the heart sound in term of time and frequency simultaneously which while the MFCC defines a signal in term of frequency coefficient corresponding to the Mel filter scale. There are 100 normal data and 100 data with disease obtained from the hospital which consists of various kinds of problems including mitral regurgitation and stenosis, tricuspid regurgitation and stenosis, ventricular septal defect and other structural related disease. B-Distribution is chosen from a number of time-frequency analysis methods due its capability to represent the signal in the most efficient way in term of noise and cross term reduction. The advantage of MFCC is that it is good in error reduction and able to produce a robust feature when the signal is affected by noise. SVD/PCA technique is used to extract the important features out of the B-Distribution representation. The coefficient obtained from SVD-PCA and MFCC is later used for classification Artificial Neural Network. The results show that the system is able to produce the accuracy up to 90.0% using the TFD and 80.0% using the MFCC.